Filtering of anomalous weather events and tracing their behavior

Every year different parts of the world are affected by anomalous weather events like heavy rainfall, drought and snowfall. As it affects the life of people, the prediction of extreme events and tracing their behaviors at an earlier stage is more important in the field of meteorology. In this paper, the anomalous weather events are filtered by using the Anomaly Frequency Method (AFM). The tracing of the extreme weather events are made by using the subspace clustering method. Based on the distance based quality function the best cluster which has been traced is evaluated. Cluster tracing describes about the similarity behaviour tracing over time. The nature of the anomalous weather events and their movement in different dimensional spaces are described using the subspace cluster tracing method. These clusters are approximated by the construction of hypercube which results in improved tracing of the weather events. Therefore this method provides better results for the filtering and tracing of the anomalous weather events.